Overview

Dataset statistics

Number of variables36
Number of observations5000
Missing cells8939
Missing cells (%)5.0%
Duplicate rows537
Duplicate rows (%)10.7%
Total size in memory1.4 MiB
Average record size in memory288.0 B

Variable types

Categorical11
Unsupported4
DateTime5
Numeric15
Boolean1

Alerts

item_tax has constant value "0" Constant
transport_cost_actual has constant value "0" Constant
Dataset has 537 (10.7%) duplicate rowsDuplicates
ordoro_id has a high cardinality: 3165 distinct values High cardinality
sku has a high cardinality: 930 distinct values High cardinality
item_sale_price is highly correlated with total_per_sku and 3 other fieldsHigh correlation
shipping_sale is highly correlated with mkp_actual_commissionHigh correlation
total_per_sku is highly correlated with item_sale_price and 3 other fieldsHigh correlation
item_cost is highly correlated with item_sale_price and 3 other fieldsHigh correlation
mage_mkp_commission is highly correlated with mkp_actual_commissionHigh correlation
mkp_estimated_commission is highly correlated with mkp_actual_commissionHigh correlation
mkp_actual_commission is highly correlated with shipping_sale and 2 other fieldsHigh correlation
order_grand_total is highly correlated with item_sale_price and 3 other fieldsHigh correlation
order_place_time is highly correlated with order_total_timeHigh correlation
order_total_time is highly correlated with order_place_timeHigh correlation
gm_estimated is highly correlated with item_sale_price and 3 other fieldsHigh correlation
item_sale_price is highly correlated with total_per_sku and 4 other fieldsHigh correlation
total_per_sku is highly correlated with item_sale_price and 4 other fieldsHigh correlation
item_cost is highly correlated with item_sale_price and 2 other fieldsHigh correlation
mkp_actual_commission is highly correlated with item_sale_price and 1 other fieldsHigh correlation
order_grand_total is highly correlated with item_sale_price and 3 other fieldsHigh correlation
order_place_time is highly correlated with order_total_timeHigh correlation
order_total_time is highly correlated with order_place_timeHigh correlation
gm_estimated is highly correlated with item_sale_price and 2 other fieldsHigh correlation
item_sale_price is highly correlated with total_per_sku and 3 other fieldsHigh correlation
total_per_sku is highly correlated with item_sale_price and 3 other fieldsHigh correlation
item_cost is highly correlated with item_sale_price and 2 other fieldsHigh correlation
mkp_estimated_commission is highly correlated with mkp_actual_commissionHigh correlation
mkp_actual_commission is highly correlated with mkp_estimated_commissionHigh correlation
order_grand_total is highly correlated with item_sale_price and 2 other fieldsHigh correlation
order_place_time is highly correlated with order_total_timeHigh correlation
order_total_time is highly correlated with order_place_timeHigh correlation
gm_estimated is highly correlated with item_sale_price and 1 other fieldsHigh correlation
supplier_name is highly correlated with item_tax and 1 other fieldsHigh correlation
courier is highly correlated with item_tax and 1 other fieldsHigh correlation
cd_status is highly correlated with item_tax and 3 other fieldsHigh correlation
item_tax is highly correlated with supplier_name and 8 other fieldsHigh correlation
transport_cost_actual is highly correlated with supplier_name and 8 other fieldsHigh correlation
is_clogistique_order is highly correlated with cd_status and 3 other fieldsHigh correlation
order_status is highly correlated with item_tax and 2 other fieldsHigh correlation
line_type is highly correlated with item_tax and 1 other fieldsHigh correlation
mkp_name is highly correlated with cd_status and 3 other fieldsHigh correlation
line_status is highly correlated with item_tax and 2 other fieldsHigh correlation
mkp_name is highly correlated with supplier_name and 4 other fieldsHigh correlation
item_sale_price is highly correlated with shipping_sale and 9 other fieldsHigh correlation
shipping_sale is highly correlated with item_sale_price and 5 other fieldsHigh correlation
total_per_sku is highly correlated with item_sale_price and 9 other fieldsHigh correlation
supplier_name is highly correlated with mkp_name and 9 other fieldsHigh correlation
order_status is highly correlated with cancelled_value and 7 other fieldsHigh correlation
item_cost is highly correlated with item_sale_price and 7 other fieldsHigh correlation
cancelled_value is highly correlated with order_status and 3 other fieldsHigh correlation
transport_cost_est is highly correlated with item_sale_price and 8 other fieldsHigh correlation
courier is highly correlated with item_sale_price and 3 other fieldsHigh correlation
mage_mkp_commission is highly correlated with shipping_sale and 2 other fieldsHigh correlation
mkp_estimated_commission is highly correlated with mkp_name and 7 other fieldsHigh correlation
mkp_actual_commission is highly correlated with item_sale_price and 7 other fieldsHigh correlation
order_grand_total is highly correlated with item_sale_price and 8 other fieldsHigh correlation
max_shipping_date is highly correlated with mkp_name and 7 other fieldsHigh correlation
max_delivery_date is highly correlated with mkp_name and 7 other fieldsHigh correlation
cd_status is highly correlated with order_status and 6 other fieldsHigh correlation
is_clogistique_order is highly correlated with mkp_name and 2 other fieldsHigh correlation
line_status is highly correlated with order_status and 7 other fieldsHigh correlation
order_place_time is highly correlated with max_shipping_date and 2 other fieldsHigh correlation
order_delivery_time is highly correlated with order_status and 2 other fieldsHigh correlation
order_total_time is highly correlated with max_shipping_date and 3 other fieldsHigh correlation
gm_estimated is highly correlated with item_sale_price and 11 other fieldsHigh correlation
title has 5000 (100.0%) missing values Missing
courier has 1583 (31.7%) missing values Missing
cd_status has 2356 (47.1%) missing values Missing
ordoro_id is uniformly distributed Uniform
mkp_order_no is an unsupported type, check if it needs cleaning or further analysis Unsupported
title is an unsupported type, check if it needs cleaning or further analysis Unsupported
first_pick_date is an unsupported type, check if it needs cleaning or further analysis Unsupported
last_delivery_date is an unsupported type, check if it needs cleaning or further analysis Unsupported
shipping_sale has 1963 (39.3%) zeros Zeros
item_cost has 74 (1.5%) zeros Zeros
cancelled_value has 4928 (98.6%) zeros Zeros
transport_cost_est has 2463 (49.3%) zeros Zeros
mage_mkp_commission has 3895 (77.9%) zeros Zeros
mkp_estimated_commission has 2675 (53.5%) zeros Zeros
mkp_actual_commission has 2356 (47.1%) zeros Zeros
order_place_time has 157 (3.1%) zeros Zeros
order_prep_time has 2063 (41.3%) zeros Zeros
order_delivery_time has 2212 (44.2%) zeros Zeros
order_total_time has 157 (3.1%) zeros Zeros

Reproduction

Analysis started2022-06-24 11:54:47.204783
Analysis finished2022-06-24 11:55:37.303956
Duration50.1 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

line_type
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
ORDER LINE
4980 
FULL REPLACEMENT
 
13
PARTIAL REPLACEMENT
 
7

Length

Max length19
Median length10
Mean length10.0282
Min length10

Characters and Unicode

Total characters50141
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowORDER LINE
2nd rowORDER LINE
3rd rowORDER LINE
4th rowORDER LINE
5th rowORDER LINE

Common Values

ValueCountFrequency (%)
ORDER LINE4980
99.6%
FULL REPLACEMENT13
 
0.3%
PARTIAL REPLACEMENT7
 
0.1%

Length

2022-06-24T14:55:37.445748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-24T14:55:37.621279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
order4980
49.8%
line4980
49.8%
replacement20
 
0.2%
full13
 
0.1%
partial7
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E10020
20.0%
R9987
19.9%
L5033
10.0%
5000
10.0%
N5000
10.0%
I4987
9.9%
O4980
9.9%
D4980
9.9%
A34
 
0.1%
P27
 
0.1%
Other values (5)93
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter45141
90.0%
Space Separator5000
 
10.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E10020
22.2%
R9987
22.1%
L5033
11.1%
N5000
11.1%
I4987
11.0%
O4980
11.0%
D4980
11.0%
A34
 
0.1%
P27
 
0.1%
T27
 
0.1%
Other values (4)66
 
0.1%
Space Separator
ValueCountFrequency (%)
5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin45141
90.0%
Common5000
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E10020
22.2%
R9987
22.1%
L5033
11.1%
N5000
11.1%
I4987
11.0%
O4980
11.0%
D4980
11.0%
A34
 
0.1%
P27
 
0.1%
T27
 
0.1%
Other values (4)66
 
0.1%
Common
ValueCountFrequency (%)
5000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII50141
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E10020
20.0%
R9987
19.9%
L5033
10.0%
5000
10.0%
N5000
10.0%
I4987
9.9%
O4980
9.9%
D4980
9.9%
A34
 
0.1%
P27
 
0.1%
Other values (5)93
 
0.2%

mkp_name
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
CDiscount
2219 
Monechelle
1516 
conforama
681 
CLogistique
427 
Fnac
 
107
Other values (3)
 
50

Length

Max length13
Median length9
Mean length9.367
Min length4

Characters and Unicode

Total characters46835
Distinct characters24
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCDiscount
2nd rowCDiscount
3rd rowPriceMinister
4th rowMonechelle
5th rowMonechelle

Common Values

ValueCountFrequency (%)
CDiscount2219
44.4%
Monechelle1516
30.3%
conforama681
 
13.6%
CLogistique427
 
8.5%
Fnac107
 
2.1%
Darty25
 
0.5%
RueDuCommerce16
 
0.3%
PriceMinister9
 
0.2%

Length

2022-06-24T14:55:37.713071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-24T14:55:37.824773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
cdiscount2219
44.4%
monechelle1516
30.3%
conforama681
 
13.6%
clogistique427
 
8.5%
fnac107
 
2.1%
darty25
 
0.5%
rueducommerce16
 
0.3%
priceminister9
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o5540
11.8%
e5041
10.8%
c4548
9.7%
n4532
9.7%
i3100
 
6.6%
l3032
 
6.5%
t2680
 
5.7%
u2678
 
5.7%
C2662
 
5.7%
s2655
 
5.7%
Other values (14)10367
22.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter39829
85.0%
Uppercase Letter7006
 
15.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o5540
13.9%
e5041
12.7%
c4548
11.4%
n4532
11.4%
i3100
7.8%
l3032
7.6%
t2680
6.7%
u2678
6.7%
s2655
6.7%
h1516
 
3.8%
Other values (7)4507
11.3%
Uppercase Letter
ValueCountFrequency (%)
C2662
38.0%
D2260
32.3%
M1525
21.8%
L427
 
6.1%
F107
 
1.5%
R16
 
0.2%
P9
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin46835
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o5540
11.8%
e5041
10.8%
c4548
9.7%
n4532
9.7%
i3100
 
6.6%
l3032
 
6.5%
t2680
 
5.7%
u2678
 
5.7%
C2662
 
5.7%
s2655
 
5.7%
Other values (14)10367
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII46835
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o5540
11.8%
e5041
10.8%
c4548
9.7%
n4532
9.7%
i3100
 
6.6%
l3032
 
6.5%
t2680
 
5.7%
u2678
 
5.7%
C2662
 
5.7%
s2655
 
5.7%
Other values (14)10367
22.1%

mkp_order_no
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size39.2 KiB
Distinct2928
Distinct (%)58.6%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Minimum2020-09-08 20:30:00
Maximum2020-10-03 09:22:00
2022-06-24T14:55:37.972884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:38.094591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ordoro_id
Categorical

HIGH CARDINALITY
UNIFORM

Distinct3165
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
1-300057096
 
8
1-300054839
 
8
1-300057378
 
8
1-300057368
 
8
1-300056841
 
8
Other values (3160)
4960 

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters55000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1609 ?
Unique (%)32.2%

Sample

1st row1-300055647
2nd row1-300055647
3rd row1-300056586
4th row1-300056600
5th row1-300056647

Common Values

ValueCountFrequency (%)
1-3000570968
 
0.2%
1-3000548398
 
0.2%
1-3000573788
 
0.2%
1-3000573688
 
0.2%
1-3000568418
 
0.2%
1-3000566798
 
0.2%
1-3000557436
 
0.1%
1-3000557816
 
0.1%
1-3000556116
 
0.1%
1-3000556796
 
0.1%
Other values (3155)4928
98.6%

Length

2022-06-24T14:55:38.211246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1-3000570968
 
0.2%
1-3000573788
 
0.2%
1-3000573688
 
0.2%
1-3000568418
 
0.2%
1-3000566798
 
0.2%
1-3000548398
 
0.2%
1-3000548286
 
0.1%
1-3000563886
 
0.1%
1-3000548436
 
0.1%
1-3000570016
 
0.1%
Other values (3155)4928
98.6%

Most occurring characters

ValueCountFrequency (%)
016541
30.1%
57991
14.5%
16548
 
11.9%
36443
 
11.7%
-5000
 
9.1%
73141
 
5.7%
62828
 
5.1%
41818
 
3.3%
81692
 
3.1%
91571
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number50000
90.9%
Dash Punctuation5000
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
016541
33.1%
57991
16.0%
16548
 
13.1%
36443
 
12.9%
73141
 
6.3%
62828
 
5.7%
41818
 
3.6%
81692
 
3.4%
91571
 
3.1%
21427
 
2.9%
Dash Punctuation
ValueCountFrequency (%)
-5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common55000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
016541
30.1%
57991
14.5%
16548
 
11.9%
36443
 
11.7%
-5000
 
9.1%
73141
 
5.7%
62828
 
5.1%
41818
 
3.3%
81692
 
3.1%
91571
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII55000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
016541
30.1%
57991
14.5%
16548
 
11.9%
36443
 
11.7%
-5000
 
9.1%
73141
 
5.7%
62828
 
5.1%
41818
 
3.3%
81692
 
3.1%
91571
 
2.9%

sku
Categorical

HIGH CARDINALITY

Distinct930
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
TOP-MalwaM6/140/white
 
366
TOP-MalwaM3/white
 
143
AKO-K120-6SZ-BIALA
 
108
TOP-MalwaM6/140/sonoma
 
93
AKO-K60-5SZ-BIALA
 
83
Other values (925)
4207 

Length

Max length50
Median length43
Mean length20.2334
Min length7

Characters and Unicode

Total characters101167
Distinct characters67
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique279 ?
Unique (%)5.6%

Sample

1st rowKK-KKLJOYBLU00000
2nd rowKK-KKLJOYBLU00000
3rd rowKK-KKWMOOVNAV0000
4th rowKK-KKLJOYBLU000AC
5th rowKK-KKLJOYBLU000AC

Common Values

ValueCountFrequency (%)
TOP-MalwaM6/140/white366
 
7.3%
TOP-MalwaM3/white143
 
2.9%
AKO-K120-6SZ-BIALA108
 
2.2%
TOP-MalwaM6/140/sonoma93
 
1.9%
AKO-K60-5SZ-BIALA83
 
1.7%
AKO-K120-8SZ-BIALA78
 
1.6%
TOP-S33/sonoma78
 
1.6%
TOP-2D4S/120/dab_artisan71
 
1.4%
TOP-S33/white71
 
1.4%
TOP-MalwaM4/white56
 
1.1%
Other values (920)3853
77.1%

Length

2022-06-24T14:55:38.347912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
top-malwam6/140/white366
 
7.1%
top-malwam3/white143
 
2.8%
ako-k120-6sz-biala108
 
2.1%
top-malwam6/140/sonoma93
 
1.8%
ako-k60-5sz-biala83
 
1.6%
ako-k120-8sz-biala78
 
1.5%
top-s33/sonoma78
 
1.5%
top-2d4s/120/dab_artisan71
 
1.4%
top-s33/white71
 
1.4%
top-malwam2/white59
 
1.2%
Other values (921)3973
77.6%

Most occurring characters

ValueCountFrequency (%)
-11974
 
11.8%
O5810
 
5.7%
A5108
 
5.0%
a4573
 
4.5%
M3611
 
3.6%
P3385
 
3.3%
K3233
 
3.2%
03231
 
3.2%
T3231
 
3.2%
/2934
 
2.9%
Other values (57)54077
53.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter44649
44.1%
Lowercase Letter27356
27.0%
Decimal Number13791
 
13.6%
Dash Punctuation11974
 
11.8%
Other Punctuation3018
 
3.0%
Connector Punctuation256
 
0.3%
Space Separator123
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O5810
13.0%
A5108
11.4%
M3611
 
8.1%
P3385
 
7.6%
K3233
 
7.2%
T3231
 
7.2%
S2616
 
5.9%
L2223
 
5.0%
B2217
 
5.0%
I1838
 
4.1%
Other values (16)11377
25.5%
Lowercase Letter
ValueCountFrequency (%)
a4573
16.7%
e2520
9.2%
i2295
8.4%
w2280
8.3%
o2260
8.3%
t1992
 
7.3%
l1815
 
6.6%
n1657
 
6.1%
h1585
 
5.8%
s1209
 
4.4%
Other values (16)5170
18.9%
Decimal Number
ValueCountFrequency (%)
03231
23.4%
12655
19.3%
21725
12.5%
41620
11.7%
61545
11.2%
31351
9.8%
5643
 
4.7%
8518
 
3.8%
7277
 
2.0%
9226
 
1.6%
Other Punctuation
ValueCountFrequency (%)
/2934
97.2%
.84
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
-11974
100.0%
Connector Punctuation
ValueCountFrequency (%)
_256
100.0%
Space Separator
ValueCountFrequency (%)
123
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin72005
71.2%
Common29162
28.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
O5810
 
8.1%
A5108
 
7.1%
a4573
 
6.4%
M3611
 
5.0%
P3385
 
4.7%
K3233
 
4.5%
T3231
 
4.5%
S2616
 
3.6%
e2520
 
3.5%
i2295
 
3.2%
Other values (42)35623
49.5%
Common
ValueCountFrequency (%)
-11974
41.1%
03231
 
11.1%
/2934
 
10.1%
12655
 
9.1%
21725
 
5.9%
41620
 
5.6%
61545
 
5.3%
31351
 
4.6%
5643
 
2.2%
8518
 
1.8%
Other values (5)966
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII101167
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-11974
 
11.8%
O5810
 
5.7%
A5108
 
5.0%
a4573
 
4.5%
M3611
 
3.6%
P3385
 
3.3%
K3233
 
3.2%
03231
 
3.2%
T3231
 
3.2%
/2934
 
2.9%
Other values (57)54077
53.5%

item_sale_price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct257
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.089582
Minimum0
Maximum869.95
Zeros14
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-24T14:55:38.482520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44.95
Q177.95
median99.95
Q3139.95
95-th percentile259.95
Maximum869.95
Range869.95
Interquartile range (IQR)62

Descriptive statistics

Standard deviation94.96599609
Coefficient of variation (CV)0.7531629068
Kurtosis17.02785374
Mean126.089582
Median Absolute Deviation (MAD)30
Skewness3.629804934
Sum630447.91
Variance9018.540414
MonotonicityNot monotonic
2022-06-24T14:55:38.641130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89.95563
 
11.3%
99.95218
 
4.4%
74.95214
 
4.3%
79.95195
 
3.9%
114.95167
 
3.3%
119.95164
 
3.3%
134.95141
 
2.8%
139.95135
 
2.7%
39.95123
 
2.5%
109.95120
 
2.4%
Other values (247)2960
59.2%
ValueCountFrequency (%)
014
0.3%
9.92
 
< 0.1%
12.951
 
< 0.1%
16.952
 
< 0.1%
19.951
 
< 0.1%
22.91
 
< 0.1%
22.955
 
0.1%
24.952
 
< 0.1%
26.951
 
< 0.1%
29.051
 
< 0.1%
ValueCountFrequency (%)
869.953
0.1%
829.951
 
< 0.1%
759.953
0.1%
757.952
 
< 0.1%
751.952
 
< 0.1%
744.951
 
< 0.1%
708.952
 
< 0.1%
706.952
 
< 0.1%
699.957
0.1%
688.952
 
< 0.1%

item_tax
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
0
5000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
05000
100.0%

Length

2022-06-24T14:55:38.753827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-24T14:55:38.833613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
05000
100.0%

Most occurring characters

ValueCountFrequency (%)
05000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05000
100.0%

shipping_sale
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct30
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.743202
Minimum0
Maximum100
Zeros1963
Zeros (%)39.3%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-24T14:55:38.911405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9.95
Q319.95
95-th percentile19.95
Maximum100
Range100
Interquartile range (IQR)19.95

Descriptive statistics

Standard deviation9.026153024
Coefficient of variation (CV)0.9264052027
Kurtosis0.8130880075
Mean9.743202
Median Absolute Deviation (MAD)9.95
Skewness0.4013176443
Sum48716.01
Variance81.47143841
MonotonicityNot monotonic
2022-06-24T14:55:39.016127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
01963
39.3%
19.951801
36.0%
9.95973
19.5%
4.9862
 
1.2%
12.4844
 
0.9%
14.9520
 
0.4%
9.9916
 
0.3%
19.9916
 
0.3%
3.9913
 
0.3%
9.9812
 
0.2%
Other values (20)80
 
1.6%
ValueCountFrequency (%)
01963
39.3%
0.972
 
< 0.1%
1.22
 
< 0.1%
1.271
 
< 0.1%
1.56
 
0.1%
2.072
 
< 0.1%
2.711
 
< 0.1%
2.995
 
0.1%
3.323
 
0.1%
3.52
 
< 0.1%
ValueCountFrequency (%)
1001
 
< 0.1%
502
 
< 0.1%
49.954
 
0.1%
39.955
 
0.1%
34.956
 
0.1%
24.954
 
0.1%
19.9916
 
0.3%
19.951801
36.0%
14.982
 
< 0.1%
14.9520
 
0.4%

total_per_sku
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct426
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.832784
Minimum0
Maximum919.9
Zeros9
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-24T14:55:39.136770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49.95
Q179.95
median109.9
Q3151.995
95-th percentile269.9015
Maximum919.9
Range919.9
Interquartile range (IQR)72.045

Descriptive statistics

Standard deviation96.76917272
Coefficient of variation (CV)0.7124139686
Kurtosis16.50259543
Mean135.832784
Median Absolute Deviation (MAD)31.95
Skewness3.527433161
Sum679163.92
Variance9364.272788
MonotonicityNot monotonic
2022-06-24T14:55:39.294381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109.9343
 
6.9%
74.95167
 
3.3%
134.9141
 
2.8%
99.9117
 
2.3%
89.95109
 
2.2%
139.9109
 
2.2%
99.95103
 
2.1%
77.95102
 
2.0%
89.9101
 
2.0%
79.9592
 
1.8%
Other values (416)3616
72.3%
ValueCountFrequency (%)
09
0.2%
9.994
0.1%
19.852
 
< 0.1%
19.951
 
< 0.1%
22.91
 
< 0.1%
24.952
 
< 0.1%
29.91
 
< 0.1%
29.953
 
0.1%
32.851
 
< 0.1%
32.95
0.1%
ValueCountFrequency (%)
919.92
 
< 0.1%
889.91
 
< 0.1%
849.91
 
< 0.1%
779.93
0.1%
777.92
 
< 0.1%
764.91
 
< 0.1%
751.952
 
< 0.1%
728.92
 
< 0.1%
719.96
0.1%
706.952
 
< 0.1%

title
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing5000
Missing (%)100.0%
Memory size39.2 KiB

supplier_name
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct37
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
TOP
2408 
AKO
1234 
MOB
412 
KD
 
169
HAA
 
79
Other values (32)
698 

Length

Max length5
Median length3
Mean length2.9842
Min length2

Characters and Unicode

Total characters14921
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowKK
2nd rowKK
3rd rowKK
4th rowKK
5th rowKK

Common Values

ValueCountFrequency (%)
TOP2408
48.2%
AKO1234
24.7%
MOB412
 
8.2%
KD169
 
3.4%
HAA79
 
1.6%
MAL72
 
1.4%
REG71
 
1.4%
BEL68
 
1.4%
CARE65
 
1.3%
PM50
 
1.0%
Other values (27)372
 
7.4%

Length

2022-06-24T14:55:39.417423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
top2408
48.2%
ako1234
24.7%
mob412
 
8.2%
kd169
 
3.4%
haa79
 
1.6%
mal72
 
1.4%
reg71
 
1.4%
bel68
 
1.4%
care65
 
1.3%
pm50
 
1.0%
Other values (27)372
 
7.4%

Most occurring characters

ValueCountFrequency (%)
O4180
28.0%
P2459
16.5%
T2448
16.4%
A1545
 
10.4%
K1463
 
9.8%
M622
 
4.2%
B530
 
3.6%
E224
 
1.5%
D208
 
1.4%
L176
 
1.2%
Other values (12)1066
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter14921
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O4180
28.0%
P2459
16.5%
T2448
16.4%
A1545
 
10.4%
K1463
 
9.8%
M622
 
4.2%
B530
 
3.6%
E224
 
1.5%
D208
 
1.4%
L176
 
1.2%
Other values (12)1066
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Latin14921
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O4180
28.0%
P2459
16.5%
T2448
16.4%
A1545
 
10.4%
K1463
 
9.8%
M622
 
4.2%
B530
 
3.6%
E224
 
1.5%
D208
 
1.4%
L176
 
1.2%
Other values (12)1066
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII14921
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O4180
28.0%
P2459
16.5%
T2448
16.4%
A1545
 
10.4%
K1463
 
9.8%
M622
 
4.2%
B530
 
3.6%
E224
 
1.5%
D208
 
1.4%
L176
 
1.2%
Other values (12)1066
 
7.1%

order_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
shipped
3551 
awaiting_fulfillment
1377 
cancelled
 
72

Length

Max length20
Median length7
Mean length10.609
Min length7

Characters and Unicode

Total characters53045
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowshipped
2nd rowshipped
3rd rowshipped
4th rowshipped
5th rowshipped

Common Values

ValueCountFrequency (%)
shipped3551
71.0%
awaiting_fulfillment1377
 
27.5%
cancelled72
 
1.4%

Length

2022-06-24T14:55:39.534309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-24T14:55:39.642948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
shipped3551
71.0%
awaiting_fulfillment1377
 
27.5%
cancelled72
 
1.4%

Most occurring characters

ValueCountFrequency (%)
i7682
14.5%
p7102
13.4%
e5072
9.6%
l4275
8.1%
d3623
 
6.8%
s3551
 
6.7%
h3551
 
6.7%
a2826
 
5.3%
n2826
 
5.3%
f2754
 
5.2%
Other values (7)9783
18.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter51668
97.4%
Connector Punctuation1377
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i7682
14.9%
p7102
13.7%
e5072
9.8%
l4275
8.3%
d3623
7.0%
s3551
6.9%
h3551
6.9%
a2826
 
5.5%
n2826
 
5.5%
f2754
 
5.3%
Other values (6)8406
16.3%
Connector Punctuation
ValueCountFrequency (%)
_1377
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin51668
97.4%
Common1377
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i7682
14.9%
p7102
13.7%
e5072
9.8%
l4275
8.3%
d3623
7.0%
s3551
6.9%
h3551
6.9%
a2826
 
5.5%
n2826
 
5.5%
f2754
 
5.3%
Other values (6)8406
16.3%
Common
ValueCountFrequency (%)
_1377
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII53045
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i7682
14.5%
p7102
13.4%
e5072
9.6%
l4275
8.1%
d3623
 
6.8%
s3551
 
6.7%
h3551
 
6.7%
a2826
 
5.3%
n2826
 
5.3%
f2754
 
5.2%
Other values (7)9783
18.4%

item_cost
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct462
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.622798
Minimum0
Maximum333.78
Zeros74
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-24T14:55:39.791693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13.78
Q124.87
median39.08
Q350.97
95-th percentile89.53
Maximum333.78
Range333.78
Interquartile range (IQR)26.1

Descriptive statistics

Standard deviation28.9859361
Coefficient of variation (CV)0.6800570929
Kurtosis17.6382357
Mean42.622798
Median Absolute Deviation (MAD)13.32
Skewness3.363337706
Sum213113.99
Variance840.1844914
MonotonicityNot monotonic
2022-06-24T14:55:39.954264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43.95510
 
10.2%
24.87226
 
4.5%
23.98178
 
3.6%
24.33136
 
2.7%
39.16125
 
2.5%
35.59119
 
2.4%
12.17110
 
2.2%
30.24109
 
2.2%
65.99103
 
2.1%
50.97103
 
2.1%
Other values (452)3281
65.6%
ValueCountFrequency (%)
074
1.5%
0.991
 
< 0.1%
4.532
 
< 0.1%
4.71
 
< 0.1%
5.12
 
< 0.1%
6.461
 
< 0.1%
6.492
 
< 0.1%
7.655
 
0.1%
7.993
 
0.1%
8.716
 
0.1%
ValueCountFrequency (%)
333.781
 
< 0.1%
303.121
 
< 0.1%
264.41
 
< 0.1%
259.122
 
< 0.1%
256.892
 
< 0.1%
242.542
 
< 0.1%
2312
 
< 0.1%
221.051
 
< 0.1%
216.457
0.1%
214.866
0.1%

cancelled_value
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct44
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.016876
Minimum0
Maximum741.58
Zeros4928
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-24T14:55:40.078931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum741.58
Range741.58
Interquartile range (IQR)0

Descriptive statistics

Standard deviation21.07987649
Coefficient of variation (CV)10.45174641
Kurtosis432.6065269
Mean2.016876
Median Absolute Deviation (MAD)0
Skewness17.3687368
Sum10084.38
Variance444.3611927
MonotonicityNot monotonic
2022-06-24T14:55:40.192442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
04928
98.6%
105.754
 
0.1%
166.634
 
0.1%
149.924
 
0.1%
112.462
 
< 0.1%
138.182
 
< 0.1%
73.132
 
< 0.1%
109.722
 
< 0.1%
299.922
 
< 0.1%
65.752
 
< 0.1%
Other values (34)48
 
1.0%
ValueCountFrequency (%)
04928
98.6%
27.381
 
< 0.1%
42.412
 
< 0.1%
43.211
 
< 0.1%
46.281
 
< 0.1%
49.961
 
< 0.1%
58.252
 
< 0.1%
62.422
 
< 0.1%
63.252
 
< 0.1%
65.752
 
< 0.1%
ValueCountFrequency (%)
741.581
 
< 0.1%
499.921
 
< 0.1%
340.712
< 0.1%
299.922
< 0.1%
283.251
 
< 0.1%
214.882
< 0.1%
191.632
< 0.1%
183.251
 
< 0.1%
166.634
0.1%
162.422
< 0.1%

transport_cost_est
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct62
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.465766
Minimum0
Maximum177.89
Zeros2463
Zeros (%)49.3%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-24T14:55:40.331490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11.32
Q316
95-th percentile31.9
Maximum177.89
Range177.89
Interquartile range (IQR)16

Descriptive statistics

Standard deviation16.1640651
Coefficient of variation (CV)1.409767572
Kurtosis26.91198677
Mean11.465766
Median Absolute Deviation (MAD)11.32
Skewness3.74214941
Sum57328.83
Variance261.2770005
MonotonicityNot monotonic
2022-06-24T14:55:40.465164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02463
49.3%
16855
 
17.1%
15.95613
 
12.3%
29.61282
 
5.6%
31.9201
 
4.0%
35.8588
 
1.8%
30.8660
 
1.2%
11.3246
 
0.9%
11.741
 
0.8%
13.925
 
0.5%
Other values (52)326
 
6.5%
ValueCountFrequency (%)
02463
49.3%
9.058
 
0.2%
10.073
 
0.1%
10.812
 
< 0.1%
11.3116
 
0.3%
11.3246
 
0.9%
11.741
 
0.8%
11.792
 
< 0.1%
12.294
 
0.1%
12.8415
 
0.3%
ValueCountFrequency (%)
177.898
0.2%
1453
 
0.1%
114.0317
0.3%
99.838
0.2%
89.547
0.1%
87.752
 
< 0.1%
83.72
 
< 0.1%
71.71
 
< 0.1%
67.7513
0.3%
52.521
 
< 0.1%

first_pick_date
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size39.2 KiB

last_delivery_date
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size39.2 KiB

courier
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.1%
Missing1583
Missing (%)31.7%
Memory size39.2 KiB
UPS
1927 
DPD
1490 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10251
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUPS
2nd rowUPS
3rd rowDPD
4th rowDPD
5th rowUPS

Common Values

ValueCountFrequency (%)
UPS1927
38.5%
DPD1490
29.8%
(Missing)1583
31.7%

Length

2022-06-24T14:55:40.578851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-24T14:55:40.684167image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
ups1927
56.4%
dpd1490
43.6%

Most occurring characters

ValueCountFrequency (%)
P3417
33.3%
D2980
29.1%
U1927
18.8%
S1927
18.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter10251
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P3417
33.3%
D2980
29.1%
U1927
18.8%
S1927
18.8%

Most occurring scripts

ValueCountFrequency (%)
Latin10251
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P3417
33.3%
D2980
29.1%
U1927
18.8%
S1927
18.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII10251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P3417
33.3%
D2980
29.1%
U1927
18.8%
S1927
18.8%

mage_mkp_commission
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct202
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.899702
Minimum0
Maximum92.34
Zeros3895
Zeros (%)77.9%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-24T14:55:40.776884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.72
Maximum92.34
Range92.34
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.954292531
Coefficient of variation (CV)4.395113638
Kurtosis300.005005
Mean0.899702
Median Absolute Deviation (MAD)0
Skewness15.48905634
Sum4498.51
Variance15.63642942
MonotonicityNot monotonic
2022-06-24T14:55:40.957402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03895
77.9%
2.64107
 
2.1%
3.2444
 
0.9%
1.844
 
0.9%
1.8733
 
0.7%
2.8832
 
0.6%
3.3631
 
0.6%
2.431
 
0.6%
1.9231
 
0.6%
2.6326
 
0.5%
Other values (192)726
 
14.5%
ValueCountFrequency (%)
03895
77.9%
0.781
 
< 0.1%
0.792
 
< 0.1%
0.864
 
0.1%
0.891
 
< 0.1%
0.951
 
< 0.1%
0.9619
 
0.4%
14
 
0.1%
1.031
 
< 0.1%
1.042
 
< 0.1%
ValueCountFrequency (%)
92.342
 
< 0.1%
77.952
 
< 0.1%
76.751
 
< 0.1%
74.954
0.1%
306
0.1%
27.121
 
< 0.1%
17.482
 
< 0.1%
17.281
 
< 0.1%
16.974
0.1%
16.72
 
< 0.1%

mkp_estimated_commission
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct237
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.738542
Minimum0
Maximum141.09
Zeros2675
Zeros (%)53.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-24T14:55:41.126281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q317.24
95-th percentile30.59
Maximum141.09
Range141.09
Interquartile range (IQR)17.24

Descriptive statistics

Standard deviation14.05669497
Coefficient of variation (CV)1.443408569
Kurtosis11.34607209
Mean9.738542
Median Absolute Deviation (MAD)0
Skewness2.566373589
Sum48692.71
Variance197.5906734
MonotonicityNot monotonic
2022-06-24T14:55:41.251158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02675
53.5%
20.2493
 
1.9%
13.4986
 
1.7%
14.9978
 
1.6%
20.9977
 
1.5%
11.9968
 
1.4%
17.9956
 
1.1%
15.2950
 
1.0%
11.5448
 
1.0%
22.0947
 
0.9%
Other values (227)1722
34.4%
ValueCountFrequency (%)
02675
53.5%
2.542
 
< 0.1%
3.742
 
< 0.1%
4.493
 
0.1%
4.792
 
< 0.1%
4.942
 
< 0.1%
5.0918
 
0.4%
5.242
 
< 0.1%
5.431
 
< 0.1%
5.948
 
0.2%
ValueCountFrequency (%)
141.091
 
< 0.1%
112.792
 
< 0.1%
106.042
 
< 0.1%
104.991
 
< 0.1%
103.342
 
< 0.1%
102.841
 
< 0.1%
101.821
 
< 0.1%
99.741
 
< 0.1%
95.993
0.1%
93.746
0.1%

mkp_actual_commission
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct486
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.56719
Minimum0
Maximum155.09
Zeros2356
Zeros (%)47.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-24T14:55:41.379845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.49
Q313.5175
95-th percentile32.98
Maximum155.09
Range155.09
Interquartile range (IQR)13.5175

Descriptive statistics

Standard deviation15.30695413
Coefficient of variation (CV)1.599942525
Kurtosis19.42986013
Mean9.56719
Median Absolute Deviation (MAD)5.49
Skewness3.585909704
Sum47835.95
Variance234.3028447
MonotonicityNot monotonic
2022-06-24T14:55:41.495849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02356
47.1%
5.49105
 
2.1%
5.9977
 
1.5%
8.1365
 
1.3%
11.8351
 
1.0%
6.2348
 
1.0%
8.7947
 
0.9%
5.5940
 
0.8%
11.4336
 
0.7%
13.4836
 
0.7%
Other values (476)2139
42.8%
ValueCountFrequency (%)
02356
47.1%
2.972
 
< 0.1%
3.1918
 
0.4%
3.26
 
0.1%
3.431
 
< 0.1%
3.995
 
0.1%
44
 
0.1%
4.064
 
0.1%
4.153
 
0.1%
4.1610
 
0.2%
ValueCountFrequency (%)
155.092
< 0.1%
133.481
 
< 0.1%
126.812
< 0.1%
125.261
 
< 0.1%
1234
0.1%
121.082
< 0.1%
120.171
 
< 0.1%
116.983
0.1%
115.691
 
< 0.1%
114.731
 
< 0.1%

order_grand_total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct694
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161.364346
Minimum19.85
Maximum1309.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-24T14:55:41.630655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum19.85
5-th percentile67.9
Q189.85
median129.9
Q3186.95
95-th percentile384.85
Maximum1309.9
Range1290.05
Interquartile range (IQR)97.1

Descriptive statistics

Standard deviation129.3543294
Coefficient of variation (CV)0.8016289387
Kurtosis19.73849868
Mean161.364346
Median Absolute Deviation (MAD)45
Skewness3.728411865
Sum806821.73
Variance16732.54254
MonotonicityNot monotonic
2022-06-24T14:55:41.783213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109.9174
 
3.5%
89.9596
 
1.9%
99.9585
 
1.7%
89.979
 
1.6%
112.5478
 
1.6%
134.9578
 
1.6%
74.9577
 
1.5%
99.977
 
1.5%
79.969
 
1.4%
149.967
 
1.3%
Other values (684)4120
82.4%
ValueCountFrequency (%)
19.852
< 0.1%
22.91
 
< 0.1%
24.952
< 0.1%
29.91
 
< 0.1%
29.953
0.1%
32.851
 
< 0.1%
32.92
< 0.1%
33.681
 
< 0.1%
33.692
< 0.1%
34.952
< 0.1%
ValueCountFrequency (%)
1309.92
 
< 0.1%
1174.558
0.2%
1146.94
0.1%
1059.92
 
< 0.1%
1057.68
0.2%
911.512
 
< 0.1%
889.91
 
< 0.1%
879.82
 
< 0.1%
849.91
 
< 0.1%
822.852
 
< 0.1%
Distinct467
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Minimum2020-09-08 23:24:00
Maximum2020-10-03 11:40:00
2022-06-24T14:55:41.920879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:42.064460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct395
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Minimum2020-09-08 23:30:00
Maximum2020-10-03 11:45:00
2022-06-24T14:55:42.198175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:42.323805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

max_shipping_date
Date

HIGH CORRELATION

Distinct49
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Minimum2020-09-09 00:00:00
Maximum2020-11-20 00:00:00
2022-06-24T14:55:42.478574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:42.617203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)

max_delivery_date
Date

HIGH CORRELATION

Distinct58
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Minimum2020-09-11 00:00:00
Maximum2020-12-01 00:00:00
2022-06-24T14:55:42.801221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:42.946863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

cd_status
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.1%
Missing2356
Missing (%)47.1%
Memory size39.2 KiB
WaitingForShipmentAcceptation
2143 
Shipped
468 
CancelledByCustomer
 
33

Length

Max length29
Median length29
Mean length24.98108926
Min length7

Characters and Unicode

Total characters66050
Distinct characters23
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWaitingForShipmentAcceptation
2nd rowWaitingForShipmentAcceptation
3rd rowWaitingForShipmentAcceptation
4th rowWaitingForShipmentAcceptation
5th rowWaitingForShipmentAcceptation

Common Values

ValueCountFrequency (%)
WaitingForShipmentAcceptation2143
42.9%
Shipped468
 
9.4%
CancelledByCustomer33
 
0.7%
(Missing)2356
47.1%

Length

2022-06-24T14:55:43.400616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-24T14:55:43.509360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
waitingforshipmentacceptation2143
81.1%
shipped468
 
17.7%
cancelledbycustomer33
 
1.2%

Most occurring characters

ValueCountFrequency (%)
i9040
13.7%
t8605
13.0%
n6462
9.8%
p5222
 
7.9%
e4853
 
7.3%
c4319
 
6.5%
o4319
 
6.5%
a4319
 
6.5%
S2611
 
4.0%
h2611
 
4.0%
Other values (13)13689
20.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter56911
86.2%
Uppercase Letter9139
 
13.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i9040
15.9%
t8605
15.1%
n6462
11.4%
p5222
9.2%
e4853
8.5%
c4319
7.6%
o4319
7.6%
a4319
7.6%
h2611
 
4.6%
r2176
 
3.8%
Other values (7)4985
8.8%
Uppercase Letter
ValueCountFrequency (%)
S2611
28.6%
A2143
23.4%
W2143
23.4%
F2143
23.4%
C66
 
0.7%
B33
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin66050
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i9040
13.7%
t8605
13.0%
n6462
9.8%
p5222
 
7.9%
e4853
 
7.3%
c4319
 
6.5%
o4319
 
6.5%
a4319
 
6.5%
S2611
 
4.0%
h2611
 
4.0%
Other values (13)13689
20.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII66050
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i9040
13.7%
t8605
13.0%
n6462
9.8%
p5222
 
7.9%
e4853
 
7.3%
c4319
 
6.5%
o4319
 
6.5%
a4319
 
6.5%
S2611
 
4.0%
h2611
 
4.0%
Other values (13)13689
20.7%

is_clogistique_order
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
4573 
True
 
427
ValueCountFrequency (%)
False4573
91.5%
True427
 
8.5%
2022-06-24T14:55:43.636985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

line_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
Awaiting shipment
3531 
Awaiting Protocol
1397 
Cancelled
 
72

Length

Max length17
Median length17
Mean length16.8848
Min length9

Characters and Unicode

Total characters84424
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAwaiting shipment
2nd rowAwaiting shipment
3rd rowAwaiting shipment
4th rowAwaiting shipment
5th rowAwaiting shipment

Common Values

ValueCountFrequency (%)
Awaiting shipment3531
70.6%
Awaiting Protocol1397
 
27.9%
Cancelled72
 
1.4%

Length

2022-06-24T14:55:43.754443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-24T14:55:43.859883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
awaiting4928
49.6%
shipment3531
35.6%
protocol1397
 
14.1%
cancelled72
 
0.7%

Most occurring characters

ValueCountFrequency (%)
i13387
15.9%
t9856
11.7%
n8531
10.1%
a5000
 
5.9%
A4928
 
5.8%
g4928
 
5.8%
4928
 
5.8%
w4928
 
5.8%
o4191
 
5.0%
e3675
 
4.4%
Other values (10)20072
23.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter73099
86.6%
Uppercase Letter6397
 
7.6%
Space Separator4928
 
5.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i13387
18.3%
t9856
13.5%
n8531
11.7%
a5000
 
6.8%
g4928
 
6.7%
w4928
 
6.7%
o4191
 
5.7%
e3675
 
5.0%
m3531
 
4.8%
p3531
 
4.8%
Other values (6)11541
15.8%
Uppercase Letter
ValueCountFrequency (%)
A4928
77.0%
P1397
 
21.8%
C72
 
1.1%
Space Separator
ValueCountFrequency (%)
4928
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin79496
94.2%
Common4928
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i13387
16.8%
t9856
12.4%
n8531
10.7%
a5000
 
6.3%
A4928
 
6.2%
g4928
 
6.2%
w4928
 
6.2%
o4191
 
5.3%
e3675
 
4.6%
m3531
 
4.4%
Other values (9)16541
20.8%
Common
ValueCountFrequency (%)
4928
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII84424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i13387
15.9%
t9856
11.7%
n8531
10.1%
a5000
 
5.9%
A4928
 
5.8%
g4928
 
5.8%
4928
 
5.8%
w4928
 
5.8%
o4191
 
5.0%
e3675
 
4.4%
Other values (10)20072
23.8%

transport_cost_actual
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
0
5000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
05000
100.0%

Length

2022-06-24T14:55:44.021297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-24T14:55:44.144254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
05000
100.0%

Most occurring characters

ValueCountFrequency (%)
05000
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
05000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
05000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
05000
100.0%

order_place_time
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct25
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.054
Minimum0
Maximum24
Zeros157
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-24T14:55:44.225266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q313
95-th percentile23
Maximum24
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.123063358
Coefficient of variation (CV)1.009790666
Kurtosis-0.4649431463
Mean7.054
Median Absolute Deviation (MAD)2
Skewness0.9391620349
Sum35270
Variance50.73803161
MonotonicityNot monotonic
2022-06-24T14:55:44.335058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11263
25.3%
2747
14.9%
3406
 
8.1%
4231
 
4.6%
5224
 
4.5%
16210
 
4.2%
13180
 
3.6%
12176
 
3.5%
23160
 
3.2%
0157
 
3.1%
Other values (15)1246
24.9%
ValueCountFrequency (%)
0157
 
3.1%
11263
25.3%
2747
14.9%
3406
 
8.1%
4231
 
4.6%
5224
 
4.5%
6107
 
2.1%
762
 
1.2%
884
 
1.7%
998
 
2.0%
ValueCountFrequency (%)
24101
2.0%
23160
3.2%
2238
 
0.8%
2123
 
0.5%
2055
 
1.1%
19115
2.3%
1877
 
1.5%
17104
2.1%
16210
4.2%
15138
2.8%

order_prep_time
Real number (ℝ)

ZEROS

Distinct29
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3848
Minimum-15
Maximum22
Zeros2063
Zeros (%)41.3%
Negative20
Negative (%)0.4%
Memory size39.2 KiB
2022-06-24T14:55:44.446728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-15
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum22
Range37
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.260121809
Coefficient of variation (CV)1.632092583
Kurtosis17.51197933
Mean1.3848
Median Absolute Deviation (MAD)1
Skewness1.997600968
Sum6924
Variance5.10815059
MonotonicityNot monotonic
2022-06-24T14:55:44.604028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
02063
41.3%
11429
28.6%
2457
 
9.1%
3410
 
8.2%
4324
 
6.5%
592
 
1.8%
775
 
1.5%
637
 
0.7%
827
 
0.5%
1019
 
0.4%
Other values (19)67
 
1.3%
ValueCountFrequency (%)
-153
 
0.1%
-143
 
0.1%
-134
 
0.1%
-101
 
< 0.1%
-83
 
0.1%
-73
 
0.1%
-62
 
< 0.1%
-21
 
< 0.1%
02063
41.3%
11429
28.6%
ValueCountFrequency (%)
222
 
< 0.1%
196
0.1%
181
 
< 0.1%
171
 
< 0.1%
166
0.1%
152
 
< 0.1%
146
0.1%
131
 
< 0.1%
128
0.2%
116
0.1%

order_delivery_time
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct21
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9474
Minimum0
Maximum23
Zeros2212
Zeros (%)44.2%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-24T14:55:44.755585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile5
Maximum23
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.45019059
Coefficient of variation (CV)1.258185576
Kurtosis10.86742105
Mean1.9474
Median Absolute Deviation (MAD)1
Skewness2.318136007
Sum9737
Variance6.003433927
MonotonicityNot monotonic
2022-06-24T14:55:44.919150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
02212
44.2%
2736
 
14.7%
4681
 
13.6%
3462
 
9.2%
5338
 
6.8%
1329
 
6.6%
671
 
1.4%
757
 
1.1%
837
 
0.7%
1114
 
0.3%
Other values (11)63
 
1.3%
ValueCountFrequency (%)
02212
44.2%
1329
 
6.6%
2736
 
14.7%
3462
 
9.2%
4681
 
13.6%
5338
 
6.8%
671
 
1.4%
757
 
1.1%
837
 
0.7%
912
 
0.2%
ValueCountFrequency (%)
234
 
0.1%
222
 
< 0.1%
182
 
< 0.1%
175
 
0.1%
164
 
0.1%
157
0.1%
147
0.1%
139
0.2%
121
 
< 0.1%
1114
0.3%

order_total_time
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct46
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.3862
Minimum0
Maximum48
Zeros157
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2022-06-24T14:55:45.036834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median8
Q316
95-th percentile26
Maximum48
Range48
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.115480378
Coefficient of variation (CV)0.7813714716
Kurtosis0.4843135273
Mean10.3862
Median Absolute Deviation (MAD)5
Skewness0.9934974334
Sum51931
Variance65.86102176
MonotonicityNot monotonic
2022-06-24T14:55:45.164565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
3456
 
9.1%
4390
 
7.8%
5379
 
7.6%
2308
 
6.2%
8303
 
6.1%
6303
 
6.1%
9262
 
5.2%
7260
 
5.2%
10166
 
3.3%
23159
 
3.2%
Other values (36)2014
40.3%
ValueCountFrequency (%)
0157
 
3.1%
1132
 
2.6%
2308
6.2%
3456
9.1%
4390
7.8%
5379
7.6%
6303
6.1%
7260
5.2%
8303
6.1%
9262
5.2%
ValueCountFrequency (%)
484
0.1%
472
< 0.1%
462
< 0.1%
451
 
< 0.1%
431
 
< 0.1%
411
 
< 0.1%
393
0.1%
382
< 0.1%
372
< 0.1%
364
0.1%

gm_estimated
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1520
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.399984
Minimum-202.09
Maximum604.5
Zeros5
Zeros (%)0.1%
Negative121
Negative (%)2.4%
Memory size39.2 KiB
2022-06-24T14:55:45.298208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-202.09
5-th percentile7.039
Q120.3
median31.41
Q342.14
95-th percentile76.1
Maximum604.5
Range806.59
Interquartile range (IQR)21.84

Descriptive statistics

Standard deviation49.12753444
Coefficient of variation (CV)1.313571002
Kurtosis52.77797864
Mean37.399984
Median Absolute Deviation (MAD)10.78
Skewness6.177480479
Sum186999.92
Variance2413.51464
MonotonicityNot monotonic
2022-06-24T14:55:45.436634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.1481
 
1.6%
29.9462
 
1.2%
37.347
 
0.9%
32.2745
 
0.9%
29.5137
 
0.7%
29.9734
 
0.7%
33.0633
 
0.7%
26.330
 
0.6%
18.4528
 
0.6%
33.2227
 
0.5%
Other values (1510)4576
91.5%
ValueCountFrequency (%)
-202.091
< 0.1%
-124.932
< 0.1%
-124.282
< 0.1%
-124.012
< 0.1%
-116.311
< 0.1%
-111.472
< 0.1%
-111.071
< 0.1%
-109.272
< 0.1%
-100.231
< 0.1%
-98.622
< 0.1%
ValueCountFrequency (%)
604.52
< 0.1%
586.022
< 0.1%
532.943
0.1%
483.092
< 0.1%
470.792
< 0.1%
468.911
 
< 0.1%
466.042
< 0.1%
455.052
< 0.1%
454.391
 
< 0.1%
451.944
0.1%

Interactions

2022-06-24T14:55:32.706155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:04.222740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:06.557299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:08.322163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:09.989953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:11.882280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:13.851251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:15.748187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:17.482922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:19.598382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:23.699222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:25.853968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:27.475449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:29.346223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:31.050332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:32.837801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:04.615094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:06.707577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:08.459761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:10.083581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:12.015163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:13.950145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:15.842919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:17.619138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:19.784885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:23.792860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:25.947902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:27.596659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:29.452074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:31.149571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:32.940526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:04.791622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:06.823939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:08.570521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:10.187095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:12.154700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:14.079316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:15.938339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:17.793994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:19.937476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:23.991342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:26.057702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:27.727212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:29.590501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:31.244759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:33.052228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:04.901330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:06.916265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:08.664119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:10.284841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:12.266036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:14.214461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:16.030702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:17.910681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:20.147096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:24.118500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:26.174695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:27.828681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:29.724030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:31.336031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:33.204819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:05.078075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:07.017945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:08.766366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:10.388916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:12.391246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:14.346109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:16.130882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:18.055295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:20.269663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:24.230110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:26.281971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:27.944363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:29.828198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:31.443779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:33.316261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:05.204850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:07.138911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:08.877460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:10.520124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:12.519657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:14.459062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:16.395381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:18.172978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:20.455082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:24.361324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:26.390568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:28.102911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:29.933627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:31.587982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:33.426703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:05.342964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:07.268369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:08.983758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:10.795160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:12.652583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:14.566407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:16.539767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:18.344520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:20.603199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:24.519009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:26.494897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:28.219568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:30.042002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:31.723391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:33.747066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:05.514507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:07.366379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:09.077740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:10.891745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:12.786192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:14.665897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:16.655640image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:18.450324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:20.973183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:24.616037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:26.592293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:28.329191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:30.133313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:31.820862image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:33.876686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:05.626211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:07.505639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:09.185454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:10.998605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:12.977682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:14.772618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:16.759393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:18.559946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:21.888837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:24.746051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:26.706142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:28.450586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:30.233981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:31.959491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:34.012218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:05.732924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:07.649434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:09.344890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:11.111254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:13.141242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:14.891924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:16.865676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:18.700792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:22.865459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:24.905805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:26.871766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:28.611456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:30.362190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:32.077336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:34.124917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:05.837641image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:07.758288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:09.468948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:11.216800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:13.261920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:14.998522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:16.966829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:18.856369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:23.043498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:25.025162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:26.981858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:28.729825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:30.481853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:32.180385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:34.307428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:05.932585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:07.854432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:09.592942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:11.318776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:13.397086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:15.160306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:17.073188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:18.972058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:23.183397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:25.165102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:27.078618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:28.840522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:30.581986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:32.276119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:34.438078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:06.070182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:07.960284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:09.694852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:11.428107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:13.522697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:15.410234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:17.175877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:19.146591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:23.364118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:25.278617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:27.184333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:28.955768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:30.693695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:32.382054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:34.543796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:06.198327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:08.065475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:09.790049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:11.525671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:13.636038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:15.518978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:17.266645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:19.257296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:23.471030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:25.385984image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:27.273638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:29.061160image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:30.818642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:32.475821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:34.679433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:06.453258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:08.196729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:09.885755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:11.651781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:13.739057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:15.621669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:17.371607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:19.412879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:23.578607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:25.711769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:27.369931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:29.194632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:30.939479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-06-24T14:55:32.577497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-06-24T14:55:45.573271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-24T14:55:45.931311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-24T14:55:46.235637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-24T14:55:46.462031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-24T14:55:46.657476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-24T14:55:34.982398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-24T14:55:36.253042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-24T14:55:36.904739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-24T14:55:37.054480image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

line_typemkp_namemkp_order_noorder_dateordoro_idskuitem_sale_priceitem_taxshipping_saletotal_per_skutitlesupplier_nameorder_statusitem_costcancelled_valuetransport_cost_estfirst_pick_datelast_delivery_datecouriermage_mkp_commissionmkp_estimated_commissionmkp_actual_commissionorder_grand_totalordoro_import_datetool_import_datemax_shipping_datemax_delivery_datecd_statusis_clogistique_orderline_statustransport_cost_actualorder_place_timeorder_prep_timeorder_delivery_timeorder_total_timegm_estimated
0ORDER LINECDiscount2009161727KUPJE2020-09-16 15:27:001-300055647KK-KKLJOYBLU0000069.9509.9579.90NaNKKshipped0.000.00.00000-00-00 00:00:000000-00-00 00:00:00NaN1.920.0013.9081.822020-09-16 19:46:002020-09-16 19:51:002020-09-302020-10-09WaitingForShipmentAcceptationNoAwaiting shipment016001651.08
1ORDER LINECDiscount2009161727KUPJE2020-09-16 15:27:001-300055647KK-KKLJOYBLU0000069.9509.9579.90NaNKKshipped0.000.00.00000-00-00 00:00:000000-00-00 00:00:00NaN1.920.0013.9081.822020-09-16 19:46:002020-09-16 19:51:002020-09-302020-10-09WaitingForShipmentAcceptationNoAwaiting shipment016001651.08
2ORDER LINEPriceMinister3228369272020-09-24 22:25:001-300056586KK-KKWMOOVNAV0000319.9500.00319.95NaNKKshipped171.000.00.00000-00-00 00:00:000000-00-00 00:00:00NaN0.0047.990.00319.952020-09-25 00:49:002020-09-25 00:51:002020-10-052020-10-08NaNNoAwaiting shipment0800847.64
3ORDER LINEMonechelleM2009237342962020-09-25 08:47:001-300056600KK-KKLJOYBLU000AC79.9500.0079.95NaNKKshipped0.000.00.00000-00-00 00:00:000000-00-00 00:00:00NaN0.0011.990.0079.952020-09-25 12:02:002020-09-25 12:10:002020-10-022020-10-13NaNNoAwaiting shipment0800854.64
4ORDER LINEMonechelleM2009237400972020-09-25 13:11:001-300056647KK-KKLJOYBLU000AC79.9500.0079.95NaNKKshipped0.000.00.00000-00-00 00:00:000000-00-00 00:00:00NaN0.0011.990.0079.952020-09-25 15:22:002020-09-25 15:30:002020-10-022020-10-13NaNNoAwaiting shipment0700754.64
5ORDER LINEMonechelleM2009238169762020-09-28 16:05:001-300057121KK-KKKFINIBLK0000119.9500.00119.95NaNKKshipped0.000.00.00000-00-00 00:00:000000-00-00 00:00:00NaN0.0017.990.00119.952020-09-28 18:36:002020-09-28 18:45:002020-10-052020-10-14NaNNoAwaiting shipment0400481.97
6ORDER LINEMonechelleM2009238169762020-09-28 16:05:001-300057121KK-KKKFINIBLK0000119.9500.00119.95NaNKKshipped0.000.00.00000-00-00 00:00:000000-00-00 00:00:00NaN0.0017.990.00119.952020-09-28 18:36:002020-09-28 18:45:002020-10-052020-10-14NaNNoAwaiting shipment0400481.97
7ORDER LINECDiscount20092821212SYQB2020-09-28 19:21:001-300057167KK-KKFJUFIBLK000079.9509.9589.90NaNKKshipped0.000.00.00000-00-00 00:00:000000-00-00 00:00:00NaN0.000.0013.4889.902020-09-28 22:19:002020-09-28 22:30:002020-10-122020-10-21WaitingForShipmentAcceptationNoAwaiting shipment0400461.44
8ORDER LINECDiscount20092821212SYQB2020-09-28 19:21:001-300057167KK-KKFJUFIBLK000079.9509.9589.90NaNKKshipped0.000.00.00000-00-00 00:00:000000-00-00 00:00:00NaN0.000.0013.4889.902020-09-28 22:19:002020-09-28 22:30:002020-10-122020-10-21WaitingForShipmentAcceptationNoAwaiting shipment0400461.44
9ORDER LINECDiscount20090822540NKZH2020-09-08 20:54:001-300054777AKO-K120-6SZ-BIALA124.95019.95144.90NaNAKOshipped39.160.016.02020-09-10 11:15:000000-00-00 00:00:00UPS0.000.0021.73144.902020-09-09 00:33:002020-09-09 00:45:002020-09-232020-10-02WaitingForShipmentAcceptationNoAwaiting shipment0241234843.86

Last rows

line_typemkp_namemkp_order_noorder_dateordoro_idskuitem_sale_priceitem_taxshipping_saletotal_per_skutitlesupplier_nameorder_statusitem_costcancelled_valuetransport_cost_estfirst_pick_datelast_delivery_datecouriermage_mkp_commissionmkp_estimated_commissionmkp_actual_commissionorder_grand_totalordoro_import_datetool_import_datemax_shipping_datemax_delivery_datecd_statusis_clogistique_orderline_statustransport_cost_actualorder_place_timeorder_prep_timeorder_delivery_timeorder_total_timegm_estimated
4990ORDER LINEMonechelleM2009233666412020-09-09 08:03:001-300054799VOX-4011330751.9500.00751.95NaNVOXawaiting_fulfillment231.000.0145.000000-00-00 00:00:000000-00-00 00:00:00NaN0.00112.790.00751.952020-09-09 09:52:002020-09-09 10:00:002020-09-162020-09-25NaNNoAwaiting Protocol0240024137.84
4991ORDER LINECDiscount2009102124W8UBM2020-09-10 19:24:001-300055026VOX-501052179.9509.9589.90NaNVOXshipped37.000.016.000000-00-00 00:00:000000-00-00 00:00:00NaN0.000.0013.4889.902020-09-10 23:42:002020-09-10 23:45:002020-09-242020-10-05WaitingForShipmentAcceptationNoAwaiting shipment02200228.44
4992ORDER LINECDiscount20092319183OWKB2020-09-23 17:18:001-300056455VOX-4013666129.95019.95149.90NaNVOXawaiting_fulfillment43.000.035.850000-00-00 00:00:000000-00-00 00:00:00DPD3.590.0026.07153.492020-09-23 21:07:002020-09-23 21:10:002020-10-072020-10-16WaitingForShipmentAcceptationNoAwaiting Protocol0450917.01
4993ORDER LINECDiscount20092416353KX8G2020-09-24 14:35:001-300056540VOX-4010752198.95019.95218.90NaNVOXawaiting_fulfillment84.000.032.010000-00-00 00:00:000000-00-00 00:00:00DPD0.000.0032.83218.902020-09-24 18:48:002020-09-24 18:51:002020-10-082020-10-19WaitingForShipmentAcceptationNoAwaiting Protocol0350833.58
4994ORDER LINEconforama4913779302-A2020-10-01 18:30:001-300057705VOX-5010418540.95019.95560.90NaNVOXawaiting_fulfillment141.000.0145.000000-00-00 00:00:000000-00-00 00:00:00NaN0.0091.960.00560.902020-10-02 00:23:002020-10-02 00:30:002020-10-152020-10-26NaNNoAwaiting Protocol0100189.46
4995ORDER LINEMonechelleM2010239112862020-10-02 08:57:001-300057740VOX-501052189.9500.0089.95NaNVOXawaiting_fulfillment37.000.010.070000-00-00 00:00:000000-00-00 00:00:00NaN0.0013.490.0089.952020-10-02 11:45:002020-10-02 11:46:002020-10-092020-10-20NaNNoAwaiting Protocol0100114.40
4996ORDER LINECDiscount2009111343V40SI2020-09-11 11:43:001-300055062TO-SZAFKA-S33-BIEL-MAT67.8509.9577.80NaNTOshipped23.980.00.002020-09-24 21:18:002020-09-29 21:22:00DPD0.000.0011.6777.802020-09-11 15:11:002020-09-11 15:15:002020-09-252020-10-06WaitingForShipmentAcceptationNoAwaiting shipment011151729.18
4997ORDER LINEMonechelleM2009234925352020-09-14 15:44:001-300055403TO-SZAFKA-S33-BIEL-MAT79.9500.0079.95NaNTOshipped23.980.00.002020-09-24 21:15:002020-09-30 18:49:00DPD0.0011.990.0079.952020-09-14 18:16:002020-09-14 18:30:002020-09-212020-09-30NaNNoAwaiting shipment08151430.66
4998ORDER LINECDiscount2009210907BZSN12020-09-21 07:07:001-300056153TO-SZAFKA-S33-BIEL-MAT69.9509.9579.90NaNTOshipped23.980.00.002020-09-24 21:18:002020-09-29 16:27:00DPD0.000.0011.9879.902020-09-21 10:53:002020-09-21 11:00:002020-10-052020-10-14WaitingForShipmentAcceptationNoAwaiting shipment0214730.62
4999ORDER LINECDiscount2009210907BZSN12020-09-21 07:07:001-300056153TO-SZAFKA-S33-BIEL-MAT69.9509.9579.90NaNTOshipped23.980.00.002020-09-24 21:18:002020-09-29 16:27:00DPD0.000.0011.9879.902020-09-21 10:53:002020-09-21 11:00:002020-10-052020-10-14WaitingForShipmentAcceptationNoAwaiting shipment0214730.62

Duplicate rows

Most frequently occurring

line_typemkp_nameorder_dateordoro_idskuitem_sale_priceitem_taxshipping_saletotal_per_skusupplier_nameorder_statusitem_costcancelled_valuetransport_cost_estcouriermage_mkp_commissionmkp_estimated_commissionmkp_actual_commissionorder_grand_totalordoro_import_datetool_import_datemax_shipping_datemax_delivery_datecd_statusis_clogistique_orderline_statustransport_cost_actualorder_place_timeorder_prep_timeorder_delivery_timeorder_total_timegm_estimated# duplicates
23ORDER LINECDiscount2020-09-09 13:38:001-300054839TOP-R40/black57.9509.9567.90TOPshipped18.430.015.95DPD1.630.011.82278.122020-09-09 16:42:002020-09-09 16:45:002020-09-232020-10-02ShippedNoAwaiting shipment02317319.038
14ORDER LINECDiscount2020-09-09 09:52:001-300054812TOP-S33/sonoma68.9509.9578.90TOPshipped23.980.015.95DPD1.890.013.73161.582020-09-09 13:15:002020-09-09 13:30:002020-09-232020-10-02ShippedNoAwaiting shipment024142910.524
25ORDER LINECDiscount2020-09-09 13:52:001-300054843TOP-KaroK2/sonoma49.95013.2863.23TOPshipped16.820.013.67UPS2.240.016.22286.422020-09-09 16:42:002020-09-09 16:45:002020-09-232020-10-02ShippedNoAwaiting shipment02304274.124
38ORDER LINECDiscount2020-09-09 16:33:001-300054874AKO-K140-10SZ-BIALA224.95019.95244.90AKOshipped69.570.016.00UPS0.000.036.74489.802020-09-09 19:59:002020-09-09 20:00:002020-10-142020-10-23WaitingForShipmentAcceptationNoAwaiting shipment014452381.774
52ORDER LINECDiscount2020-09-09 20:38:001-300054909AKO-BIURKO-PIN-BIALE79.9509.9589.90AKOshipped28.430.015.95DPD0.000.013.49179.802020-09-09 23:24:002020-09-09 23:30:002020-09-232020-10-02ShippedNoAwaiting shipment023042717.054
112ORDER LINECDiscount2020-09-17 09:25:001-300055713TOP-S30/sonoma66.9509.9576.90TOPshipped23.080.015.95DPD0.000.011.54153.802020-09-17 12:13:002020-09-17 12:15:002020-10-012020-10-12WaitingForShipmentAcceptationNoAwaiting shipment016432313.514
146ORDER LINECDiscount2020-09-18 10:33:001-300055818AKO-K60-6SZ-BIALA-POLYSK219.95019.95239.90AKOshipped80.300.016.00UPS5.760.041.74491.312020-09-18 14:04:002020-09-18 14:10:002020-10-022020-10-13WaitingForShipmentAcceptationNoAwaiting shipment02351057.084
192ORDER LINECDiscount2020-09-20 05:45:001-300056015TOP-MalwaM6/140/white114.95034.95149.90TOPshipped43.950.016.00UPS11.400.023.84277.002020-09-20 09:11:002020-09-20 09:15:002020-10-052020-10-14WaitingForShipmentAcceptationNoAwaiting shipment013372331.634
223ORDER LINECDiscount2020-09-20 18:42:001-300056117TOP-MalwaM6/140/sonoma114.95019.95134.90TOPshipped43.950.016.00UPS0.000.020.24269.802020-09-20 21:44:002020-09-20 21:45:002020-10-022020-10-13WaitingForShipmentAcceptationNoAwaiting shipment012251932.234
263ORDER LINECDiscount2020-09-27 13:40:001-300056941AKO-K120-6SZ-BIALA89.95019.95109.90AKOshipped39.160.00.00UPS2.640.011.43225.082020-09-27 18:15:002020-09-27 18:30:002020-10-092020-10-20WaitingForShipmentAcceptationNoAwaiting shipment0513938.794